{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hide warnings if there are any\n",
    "from __future__ import print_function\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import dask\n",
    "\n",
    "import numpy.ma as ma\n",
    "\n",
    "\n",
    "from glob import glob\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "import scipy.stats as stats\n",
    "from matplotlib.patches import Polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import conda\n",
    "\n",
    "conda_file_dir = conda.__file__\n",
    "conda_dir = conda_file_dir.split('lib')[0]\n",
    "proj_lib = os.path.join(os.path.join(conda_dir, 'share'), 'proj')\n",
    "os.environ[\"PROJ_LIB\"] = proj_lib\n",
    "\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "from mpl_toolkits.basemap import shiftgrid\n",
    "\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from cartopy.util import add_cyclic_point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os.path, os, datetime\n",
    "\n",
    "from cartopy.mpl.geoaxes import GeoAxes\n",
    "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from cartopy.util import add_cyclic_point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/tigress/emcmahon/ST\n"
     ]
    }
   ],
   "source": [
    "cd /tigress/emcmahon/ST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:   (time: 1728)\n",
       "Coordinates:\n",
       "  * time      (time) object 1870-01-16 12:00:00 ... 2013-12-16 12:00:00\n",
       "    month     (time) int64 ...\n",
       "Data variables:\n",
       "    mean_sst  (time) float64 ..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ENSO index\n",
    "ENSOindex = xr.open_dataset('/tigress/emcmahon/ST/ENSOindex1.nc')\n",
    "ENSOindex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'mean_sst' (time: 768)>\n",
       "array([-1.59244449e+00, -1.51416347e+00, -1.48536283e+00, -1.41226077e+00,\n",
       "       -1.27631519e+00, -1.17312283e+00, -1.18352830e+00, -1.12023096e+00,\n",
       "       -1.21676341e+00, -1.26679782e+00, -1.37214285e+00, -1.32401796e+00,\n",
       "       -1.25538057e+00, -9.57286548e-01, -6.49305418e-01, -3.27342956e-01,\n",
       "        8.37482780e-02,  5.17611501e-01,  7.82330488e-01,  1.01767549e+00,\n",
       "        1.23015607e+00,  1.23021542e+00,  1.09062162e+00,  9.38687869e-01,\n",
       "        7.51771902e-01,  6.12068720e-01,  3.58451773e-01,  8.76853466e-02,\n",
       "       -1.20664983e-01, -2.75212719e-01, -4.44655438e-01, -3.99957085e-01,\n",
       "       -3.20103195e-01, -2.94682655e-01, -1.22622342e-01,  2.40984667e-02,\n",
       "        1.38009129e-01,  4.15950982e-01,  6.75285491e-01,  7.36474713e-01,\n",
       "        7.44883423e-01,  7.03755145e-01,  6.68228956e-01,  5.97546335e-01,\n",
       "        5.59696884e-01,  5.35472892e-01,  5.75276301e-01,  4.70458843e-01,\n",
       "        4.13380769e-01,  2.57555378e-01,  7.71590793e-02, -2.24238687e-01,\n",
       "       -5.46480897e-01, -8.45853172e-01, -1.04886045e+00, -1.18051237e+00,\n",
       "       -1.26884775e+00, -1.24267244e+00, -1.15558541e+00, -1.10149069e+00,\n",
       "       -1.06143202e+00, -1.02062535e+00, -1.09455528e+00, -1.22257489e+00,\n",
       "       -1.33699567e+00, -1.39067786e+00, -1.48893185e+00, -1.61711827e+00,\n",
       "       -1.79420340e+00, -1.89761362e+00, -1.97584108e+00, -1.90515385e+00,\n",
       "       -1.71400265e+00, -1.41300652e+00, -1.13197894e+00, -9.94014444e-01,\n",
       "       -9.48816883e-01, -9.24199204e-01, -9.49425120e-01, -9.94866635e-01,\n",
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       "       -5.08134803e-01, -1.33855010e-01,  2.03340238e-01,  5.28004894e-01,\n",
       "        8.12160772e-01,  1.00029092e+00,  1.05761644e+00,  1.13132814e+00,\n",
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       "        1.84218958e+00,  1.65835571e+00,  1.43278129e+00,  1.11118600e+00,\n",
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       "       -4.42331537e-03,  3.66902983e-02,  8.66705577e-02,  6.43241739e-02,\n",
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       "       -8.02875566e-03,  1.05325462e-01,  1.57581983e-01,  2.18522336e-01,\n",
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       "       -3.69326369e-01, -3.94074198e-01, -3.90796149e-01, -3.16582274e-01,\n",
       "       -2.42240841e-01, -2.59554161e-01, -2.64613822e-01, -2.52060853e-01,\n",
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       "       -3.05753109e-01, -1.91719880e-01, -2.81623900e-02,  1.39708472e-01,\n",
       "        4.19542770e-01,  6.83969974e-01,  8.95152658e-01,  1.10498867e+00,\n",
       "        1.26954676e+00,  1.33197523e+00,  1.29075749e+00,  1.21124526e+00,\n",
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       "       -1.22591301e-01,  1.67392602e-01,  3.42324800e-01,  5.27643104e-01,\n",
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       "        4.99715518e-01,  5.38875415e-01,  6.05546985e-01,  6.73677843e-01,\n",
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       "        2.24278879e+00,  2.31680933e+00,  2.24419151e+00,  1.97376729e+00,\n",
       "        1.60951488e+00,  1.15506785e+00,  7.07157983e-01,  2.70498037e-01,\n",
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       "        4.34234769e-01,  7.26096109e-01,  9.96367119e-01,  1.12400670e+00,\n",
       "        1.12572510e+00,  1.03113320e+00,  8.56983310e-01,  6.85322885e-01,\n",
       "        5.78449600e-01,  5.11435109e-01,  4.51921365e-01,  3.29226933e-01,\n",
       "        2.18268813e-01,  1.54402007e-01,  1.71272547e-01,  2.49400029e-01,\n",
       "        3.63885054e-01,  5.08768312e-01,  6.29289935e-01,  8.17892943e-01,\n",
       "        1.05797460e+00,  1.30942302e+00,  1.47023076e+00,  1.56239318e+00,\n",
       "        1.46501459e+00,  1.23083325e+00,  9.43807211e-01,  6.78552742e-01,\n",
       "        4.54772164e-01,  2.11693169e-01, -2.37302137e-02, -2.56690205e-01,\n",
       "       -5.00286861e-01, -7.24185176e-01, -8.27156603e-01, -8.63147360e-01,\n",
       "       -7.96343253e-01, -6.74287858e-01, -5.41489581e-01, -3.78761598e-01,\n",
       "       -2.22447112e-01, -1.65900492e-01, -1.57684139e-01, -1.50834720e-01,\n",
       "       -2.01676437e-01, -2.97506512e-01, -3.49164197e-01, -3.41641818e-01,\n",
       "       -2.70071505e-01, -7.59484613e-02,  3.08191137e-01,  7.76469094e-01,\n",
       "        1.31633953e+00,  1.87178446e+00,  2.36943002e+00,  2.76874327e+00,\n",
       "        3.11146881e+00,  3.30305290e+00,  3.41484005e+00,  3.38436484e+00,\n",
       "        3.16042728e+00,  2.75555842e+00,  2.28560340e+00,  1.54997452e+00,\n",
       "        7.88676674e-01,  1.32512451e-01, -3.85527768e-01, -8.88223025e-01,\n",
       "       -1.16476643e+00, -1.40540894e+00, -1.61586045e+00, -1.77129624e+00,\n",
       "       -1.71962911e+00, -1.59452002e+00, -1.39667291e+00, -1.21388717e+00,\n",
       "       -1.05527724e+00, -1.05107985e+00, -1.06796535e+00, -1.13342757e+00,\n",
       "       -1.26814547e+00, -1.48468177e+00, -1.72082787e+00, -1.91996325e+00,\n",
       "       -1.96889506e+00, -1.81277314e+00, -1.56640744e+00, -1.26581787e+00,\n",
       "       -9.73108696e-01, -7.34604510e-01, -6.11247759e-01, -5.58803175e-01,\n",
       "       -5.67193854e-01, -6.81000932e-01, -8.26206693e-01, -9.06277471e-01,\n",
       "       -8.81803167e-01, -7.56174388e-01, -5.53934188e-01, -3.29339336e-01,\n",
       "       -1.35122560e-01, -2.82225687e-02, -1.44422248e-02, -2.67723578e-02,\n",
       "       -8.54714200e-02, -2.18114492e-01, -2.56027668e-01, -1.99958578e-01,\n",
       "       -9.58803331e-02,  3.63516114e-02,  2.54038581e-01,  4.94732946e-01,\n",
       "        6.85965026e-01,  8.54843804e-01,  1.05035912e+00,  1.28625335e+00,\n",
       "        1.49620057e+00,  1.68867636e+00,  1.75596223e+00,  1.68774295e+00,\n",
       "        1.48849103e+00,  1.09671963e+00,  6.22627869e-01,  3.08494737e-01,\n",
       "        1.68919061e-01,  6.70726612e-02,  1.23279749e-01,  3.44511271e-01,\n",
       "        5.03733937e-01,  5.46814612e-01,  5.82307470e-01,  5.58804017e-01,\n",
       "        4.31966037e-01,  3.40933595e-01,  3.07314039e-01,  3.01595584e-01,\n",
       "        3.95022637e-01,  5.85948236e-01,  7.60541001e-01,  9.18347539e-01,\n",
       "        1.04626645e+00,  1.09973308e+00,  1.05615892e+00,  9.33439318e-01,\n",
       "        8.12516644e-01,  7.17666849e-01,  6.37658145e-01,  5.64453250e-01,\n",
       "        5.07157637e-01,  4.17407481e-01,  3.10491991e-01,  1.64858091e-01,\n",
       "       -2.50600267e-02, -2.63990043e-01, -5.27197132e-01, -7.28543825e-01,\n",
       "       -8.68228283e-01, -8.30851159e-01, -6.20699169e-01, -3.25162682e-01,\n",
       "       -7.26406436e-02,  2.19887415e-01,  4.84838797e-01,  7.17953534e-01,\n",
       "        9.68304926e-01,  1.21240708e+00,  1.26265049e+00,  1.11475918e+00,\n",
       "        8.66018795e-01,  5.46035977e-01,  1.91913980e-01, -1.77891539e-02,\n",
       "       -1.42588666e-01, -2.80514188e-01, -5.19772289e-01, -8.30557086e-01,\n",
       "       -1.21782650e+00, -1.59659010e+00, -1.93721825e+00, -2.13427867e+00,\n",
       "       -2.08363504e+00, -1.88936650e+00, -1.58280011e+00, -1.18489996e+00,\n",
       "       -7.17210350e-01, -3.92519968e-01, -2.07823525e-01, -1.22755719e-01,\n",
       "       -1.28319945e-01, -3.64699744e-01, -6.32699588e-01, -7.96920569e-01,\n",
       "       -8.99856098e-01, -8.52561639e-01, -5.39831249e-01, -1.15416394e-01,\n",
       "        3.07515135e-01,  6.76402746e-01,  9.51319527e-01,  1.17107323e+00,\n",
       "        1.44341335e+00,  1.72032807e+00,  1.93073514e+00,  2.04811716e+00,\n",
       "        2.05582866e+00,  1.79897174e+00,  1.30940211e+00,  7.56956800e-01,\n",
       "        1.86139854e-01, -4.66031546e-01, -1.05301649e+00, -1.49474458e+00,\n",
       "       -1.78438720e+00, -2.01107104e+00, -2.12300621e+00, -2.07475293e+00,\n",
       "       -1.89613191e+00, -1.65344668e+00, -1.31075674e+00, -9.07178914e-01,\n",
       "       -5.96022801e-01, -4.86340321e-01, -4.91368518e-01, -6.19849469e-01,\n",
       "       -8.49063739e-01, -1.08533789e+00, -1.18237989e+00, -1.16663964e+00,\n",
       "       -1.03741345e+00, -8.15916796e-01, -5.36113923e-01, -2.21288707e-01,\n",
       "        1.06319387e-01,  4.13328415e-01,  6.23991737e-01,  7.28200745e-01,\n",
       "        7.56051951e-01,  5.78128487e-01,  2.79566768e-01,  2.56437241e-02,\n",
       "       -9.66213485e-02, -1.95927658e-01, -2.07847696e-01, -1.68178239e-01,\n",
       "       -1.18686556e-01, -1.46391123e-01, -1.56219073e-01, -1.52350920e-01,\n",
       "       -8.45521267e-02, -5.80002424e-02, -2.28320930e-02, -2.81825901e-02])\n",
       "Coordinates:\n",
       "  * time     (time) object 1950-01-31 00:00:00 ... 2013-12-31 00:00:00"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nino = ENSOindex.sel(time=slice('1950-01-01', '2014-12-31')).resample(time='M').pad()\n",
    "\n",
    "nino = nino['mean_sst']\n",
    "nino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'precip' (time: 768, lat: 25, lon: 29)>\n",
      "array([[[           nan,            nan,            nan, ...,\n",
      "                    nan, 6.04447410e+00, 6.29221466e+00],\n",
      "        [           nan,            nan,            nan, ...,\n",
      "                    nan,            nan,            nan],\n",
      "        [           nan,            nan,            nan, ...,\n",
      "                    nan,            nan,            nan],\n",
      "        ...,\n",
      "        [2.47150892e+00, 1.64886578e+00, 1.44360371e+00, ...,\n",
      "         2.22016530e+00, 7.97234348e-01, 1.43189791e+00],\n",
      "        [2.91020757e+00, 2.08884875e+00, 1.51253832e+00, ...,\n",
      "         4.47785554e+00, 3.51994891e+00, 2.88210724e+00],\n",
      "        [2.57240357e+00,            nan,            nan, ...,\n",
      "         3.49075254e+00, 3.81285116e+00, 2.33011554e+00]],\n",
      "\n",
      "       [[           nan,            nan,            nan, ...,\n",
      "                    nan, 6.35913180e+00, 6.79100181e+00],\n",
      "        [           nan,            nan,            nan, ...,\n",
      "                    nan,            nan,            nan],\n",
      "        [           nan,            nan,            nan, ...,\n",
      "                    nan,            nan,            nan],\n",
      "        ...,\n",
      "        [8.96557621e-01, 3.63768089e-01, 8.53043835e-02, ...,\n",
      "         1.11212464e+00, 2.58485321e-01, 8.38491306e-01],\n",
      "        [1.03618554e+00, 6.06145068e-01, 2.98438298e-01, ...,\n",
      "         4.98373064e+00, 3.08083976e+00, 3.02802182e+00],\n",
      "        [1.01101311e+00,            nan,            nan, ...,\n",
      "         4.61205882e+00, 5.18943103e+00, 3.02380206e+00]],\n",
      "\n",
      "       [[           nan,            nan,            nan, ...,\n",
      "                    nan, 2.81793099e+00, 3.00684976e+00],\n",
      "        [           nan,            nan,            nan, ...,\n",
      "                    nan,            nan,            nan],\n",
      "        [           nan,            nan,            nan, ...,\n",
      "                    nan,            nan,            nan],\n",
      "        ...,\n",
      "        [1.39680281e-01, 9.36838180e-02, 5.17489567e-02, ...,\n",
      "         5.04205234e+00, 3.38204764e+00, 3.70442978e+00],\n",
      "        [1.77070802e-01, 9.69716686e-02, 1.30390778e-01, ...,\n",
      "         1.16936753e+01, 1.04425832e+01, 8.80049227e+00],\n",
      "        [2.01051440e-01,            nan,            nan, ...,\n",
      "         8.81377612e+00, 1.13078269e+01, 7.32291655e+00]],\n",
      "\n",
      "       ...,\n",
      "\n",
      "       [[           nan,            nan,            nan, ...,\n",
      "                    nan, 1.00303903e+01, 9.06230814e+00],\n",
      "        [           nan,            nan,            nan, ...,\n",
      "                    nan,            nan,            nan],\n",
      "        [           nan,            nan,            nan, ...,\n",
      "                    nan,            nan,            nan],\n",
      "        ...,\n",
      "        [9.77398406e-02, 2.36387448e-02, 1.66459121e-02, ...,\n",
      "         1.51366912e+01, 1.19135343e+01, 1.08048222e+01],\n",
      "        [1.43295880e-01, 3.35347332e-02, 2.37448209e-02, ...,\n",
      "         3.07043974e+01, 2.71806462e+01, 1.55475570e+01],\n",
      "        [3.03604778e-01,            nan,            nan, ...,\n",
      "         2.19720720e+01, 2.33283788e+01, 1.38929504e+01]],\n",
      "\n",
      "       [[           nan,            nan,            nan, ...,\n",
      "                    nan, 1.28896266e+01, 1.30231314e+01],\n",
      "        [           nan,            nan,            nan, ...,\n",
      "                    nan,            nan,            nan],\n",
      "        [           nan,            nan,            nan, ...,\n",
      "                    nan,            nan,            nan],\n",
      "        ...,\n",
      "        [1.80101312e-01, 3.53408921e-01, 4.27828787e-01, ...,\n",
      "         1.41794424e+00, 2.01039790e-01, 2.15483368e-01],\n",
      "        [1.91982325e-01, 2.90244195e-01, 6.39337287e-01, ...,\n",
      "         4.82944254e+00, 2.13247278e+00, 6.36213089e-01],\n",
      "        [1.90218020e-01,            nan,            nan, ...,\n",
      "         4.27058386e+00, 3.28825320e+00, 1.12409950e+00]],\n",
      "\n",
      "       [[           nan,            nan,            nan, ...,\n",
      "                    nan, 5.41442949e+00, 4.71809293e+00],\n",
      "        [           nan,            nan,            nan, ...,\n",
      "                    nan,            nan,            nan],\n",
      "        [           nan,            nan,            nan, ...,\n",
      "                    nan,            nan,            nan],\n",
      "        ...,\n",
      "        [2.78085930e-01, 1.19500515e-01, 2.68042969e-01, ...,\n",
      "         1.42592816e+00, 1.02058318e+00, 1.35687836e+00],\n",
      "        [1.58486580e-01, 7.15277467e-02, 2.23895669e-01, ...,\n",
      "         1.98594292e+00, 1.30401516e+00, 1.26605622e+00],\n",
      "        [3.20880018e-01,            nan,            nan, ...,\n",
      "         1.11044703e+00, 1.27003954e+00, 8.79392217e-01]]])\n",
      "Coordinates:\n",
      "  * time       (time) object 1950-01-31 00:00:00 ... 2013-12-31 00:00:00\n",
      "  * lon        (lon) float64 61.88 63.13 64.38 65.62 ... 93.12 94.38 95.62 96.88\n",
      "  * lat        (lat) float64 5.5 6.5 7.5 8.5 9.5 ... 25.5 26.5 27.5 28.5 29.5\n",
      "    mask       (lat, lon) float64 9.969e+36 9.969e+36 9.969e+36 ... 1.0 1.0 1.0\n",
      "    land_mask  (lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 1.0 1.0 1.0 1.0 1.0\n"
     ]
    }
   ],
   "source": [
    "ds4 = xr.open_dataset('/tigress/emcmahon/ST/uhi_1950_2014/Tca_max.urbanareas_amip_ENSO_analysis.nc')\n",
    "#ds = ds['t_ref']\n",
    "#ds.sel(time=slice('1960-01-01', '2013-12-31')).resample(time='M')\n",
    "#ds = ds.drop(['geolon_t', 'geolat_t'])\n",
    "#ds_mean = ds['t_ref_max'].mean(dim='time')\n",
    "#ds_mean = ds['t_ref_max'].sel(time=slice('2070-01-01', '2099-01-01')).mean(dim='time')\n",
    "#print(ds_mean)\n",
    "n1 = float(\"nan\")\n",
    "mask_urban = 1 * np.ones((ds4.dims['lat'], ds4.dims['lon'])) * np.isfinite(ds4.Tca_max.isel(time=0))  \n",
    "mask_nourban = 9.96921e+36 * np.ones((ds4.dims['lat'], ds4.dims['lon'])) * np.isnan(ds4.Tca_max.isel(time=0))  \n",
    "mask_array = mask_urban + mask_nourban\n",
    "\n",
    "\n",
    "ds3 = xr.open_dataset('/tigress/emcmahon/ST/wholeperiod/land_mask.land_mask.nc')\n",
    "\n",
    "land_mask = ds3['land_mask'] #.where(ds3.land_mask > 0.3)\n",
    "\n",
    "ds = xr.open_dataset('/tigress/emcmahon/ST/wholeperiod/precip.urban_amip_ENSO_analysis.nc')\n",
    "\n",
    "ds.coords['mask'] = (('lat', 'lon'), mask_array)\n",
    "\n",
    "ds.coords['land_mask'] = (('lat', 'lon'), land_mask)\n",
    "\n",
    "ds = ds['precip'].where(ds.land_mask > 0.1)\n",
    "\n",
    "ds = ds.where(ds.mask == 1)\n",
    "\n",
    "\n",
    "ds = ds.sel(time=slice('1950-01-01', '2014-12-31')).resample(time='M').pad()\n",
    "\n",
    "sst = ds*86400\n",
    "sst = sst.sel(lon=slice(61,97), lat=slice(5,30))\n",
    "print(sst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'time' (time: 276)>\n",
       "array([cftime.DatetimeNoLeap(1951, 6, 30, 0, 0, 0, 0, 3, 181),\n",
       "       cftime.DatetimeNoLeap(1951, 7, 31, 0, 0, 0, 0, 6, 212),\n",
       "       cftime.DatetimeNoLeap(1951, 8, 31, 0, 0, 0, 0, 2, 243), ...,\n",
       "       cftime.DatetimeNoLeap(2012, 8, 31, 0, 0, 0, 0, 0, 243),\n",
       "       cftime.DatetimeNoLeap(2012, 9, 30, 0, 0, 0, 0, 2, 273),\n",
       "       cftime.DatetimeNoLeap(2012, 10, 31, 0, 0, 0, 0, 5, 304)], dtype=object)\n",
       "Coordinates:\n",
       "  * time     (time) object 1951-06-30 00:00:00 ... 2012-10-31 00:00:00\n",
       "Attributes:\n",
       "    long_name:      time\n",
       "    axis:           T\n",
       "    calendar_type:  NOLEAP\n",
       "    bounds:         time_bnds"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst_elnino=sst[nino>=.4,:,:]\n",
    "sst_elnino['time'] #.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_amj(month):\n",
    "    #return (month >= 6) & (month <= 8) #######JJA\n",
    "    #return  (month < 4) | (month == 11) | (month == 12) #######DJF\n",
    "    return  (month < 3)  | (month == 12) #######DJF\n",
    "sst_elnino_DJF =  sst_elnino.sel(time=is_amj(sst_elnino['time.month'])) #.mean('time')\n",
    "#sst_elnino_DJF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_amj(month):\n",
    "    return (month >= 6) & (month <= 8) #######JJA\n",
    "    #return  (month < 3) | (month == 12) #######DJF\n",
    "sst_elnino_JJA =  sst_elnino.sel(time=is_amj(sst_elnino['time.month'])) #.mean('time')\n",
    "#sst_elnino_JJA['time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_amj(month):\n",
    "    return (month >= 10) & (month <= 12) #######JJA\n",
    "    #return  (month < 3) | (month == 12) #######DJF\n",
    "sst_elnino_OND =  sst_elnino.sel(time=is_amj(sst_elnino['time.month'])) #.mean('time')\n",
    "#sst_elnino_JJA['time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sst_lanina=sst[nino<=-.4,:,:]\n",
    "#sst_lanina['time'] #.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_amj(month):\n",
    "    #return (month >= 6) & (month <= 8) #######JJA\n",
    "    #return  (month < 4) | (month == 11) | (month == 12)\n",
    "    return  (month < 3)  | (month == 12) #######DJF\n",
    "sst_lanina_DJF =  sst_lanina.sel(time=is_amj(sst_lanina['time.month'])) #.mean('time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_amj(month):\n",
    "    return (month >= 6) & (month <= 8) #######JJA\n",
    "    #return  (month < 3) | (month == 12) #######DJF\n",
    "sst_lanina_JJA =  sst_lanina.sel(time=is_amj(sst_lanina['time.month'])) #.mean('time')\n",
    "#sst_lanina_JJA['time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_amj(month):\n",
    "    #return (month >= 6) & (month <= 8) #######JJA\n",
    "    #return  (month < 4) | (month == 11) | (month == 12)\n",
    "    return  (month < 3)  | (month == 12) #######DJF\n",
    "UHI_norm_DJF =  sst.sel(time=is_amj(sst['time.month'])) #.mean('time')\n",
    "#UHI_norm_DJF['time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_amj(month):\n",
    "    return (month >= 6) & (month <= 8) #######JJA\n",
    "    #return  (month < 3) | (month == 12) #######DJF\n",
    "UHI_norm_JJA =  sst.sel(time=is_amj(sst['time.month'])) #.mean('time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "precipitation_ENSO = sst_elnino_JJA.groupby('time.year').mean('time')\n",
    "precpitation_normal = UHI_norm_JJA.groupby('time.year').mean('time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'El Nino JJA Prec Anomaly w/')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sig\n",
    "ds = sst_elnino_JJA.mean('time') - UHI_norm_JJA.mean('time')\n",
    "\n",
    "#print(ds)\n",
    "\n",
    "#ds = da.sel(lon=slice(61,97), lat=slice(5,30))\n",
    "\n",
    "#print(ds)\n",
    "lat = ds.lat.values\n",
    "lon = ds.lon.values\n",
    "\n",
    "#use the subregion co-ordinates specific to your region. \n",
    "#And you need to first select the subregion before doing any calculations at all.\n",
    "#then instead of basemap and shifting the grid use this:\n",
    "#you put this immediately after calculated the mean and p values and you have defined the colormap and \n",
    "#colorbar limits. Do not use any shifting code.\n",
    "\n",
    "#t_stat, p_val = stats.ttest_ind(sst_elnino_JJA, prec_norm_JJA, equal_var=False, nan_policy='omit')\n",
    "\n",
    "t_stat, p_val = stats.ttest_ind(precipitation_ENSO, precpitation_normal, equal_var=False, nan_policy='omit')\n",
    "\n",
    "p_values_new = ma.masked_greater(p_val, 0.1)\n",
    "\n",
    "levels=np.arange(-2, 2+0.05, .5)\n",
    "\n",
    "\n",
    "proj = ccrs.PlateCarree()\n",
    "x,y = np.meshgrid(lon,lat)\n",
    "\n",
    "#f, ax = plt.subplots(nrows=1,subplot_kw=dict(projection=proj))\n",
    "f, ax = plt.subplots(nrows=1,subplot_kw=dict(projection=proj))\n",
    "\n",
    "ax.coastlines()\n",
    "\n",
    "\n",
    "\n",
    "h = ax.contourf(x, y, ds, transform=proj, cmap='BrBG', levels=levels, extend='both')\n",
    "\n",
    "plt.rcParams['hatch.color'] = 'lime'\n",
    "#ax.contourf(x, y, p_values_new,transform=proj,  hatches=[\"....\"], alpha=0)\n",
    "\n",
    "ax.contourf(x, y, p_values_new, transform=proj, hatches=[\"////\"], alpha=0)\n",
    "\n",
    "#ax.set_xticks(np.linspace(-180, 180, 5), crs=proj)\n",
    "#ax.set_yticks(np.linspace(-90, 90, 5), crs=proj)\n",
    "#ax.set_xticks(np.linspace(-130, -60, 8), crs=proj)\n",
    "#ax.set_yticks(np.linspace(20, 55, 8), crs=proj)\n",
    "\n",
    "ax.set_xticks(np.linspace(60, 95, 8), crs=proj)\n",
    "ax.set_yticks(np.linspace(5, 30, 6), crs=proj)\n",
    "lon_formatter = LongitudeFormatter()\n",
    "lat_formatter = LatitudeFormatter()\n",
    "ax.xaxis.set_major_formatter(lon_formatter)\n",
    "ax.yaxis.set_major_formatter(lat_formatter)\n",
    "\n",
    "\n",
    "\n",
    "divider = make_axes_locatable(ax)\n",
    "ax_cb = divider.new_horizontal(size=\"3%\", pad=0.3, axes_class=plt.Axes)\n",
    "\n",
    "\n",
    "f.add_axes(ax_cb)\n",
    "plt.colorbar(h, cax=ax_cb)\n",
    "\n",
    "\n",
    "#plt.rcParams['hatch.linewidth'] = 1.5\n",
    "#m.contourf(xi, yi, p_new, hatches=[\"....\"], alpha=0)\n",
    "\n",
    "#plt.rcParams['figure.facecolor'] = 'w'\n",
    "\n",
    "\n",
    "#cb = plt.colorbar(cs, orientation='horizontal', pad=0.10, shrink=0.9)\n",
    "plt.tight_layout()\n",
    "#plt.title('El Nino JJA Prec Anomaly', fontsize=16)\n",
    "ax.set_title('El Nino JJA Prec Anomaly w/', fontsize=16, y=1.0, pad=-14)\n",
    "#plt.show()\n",
    "\n",
    "#plt.savefig('/tigress/emcmahon/ST/regional_UHI_composites/JJA_elnino_Prec',dpi=600) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'El Nino DJF Prec Anomaly fixed')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sig\n",
    "ds = sst_elnino_DJF.mean('time') - UHI_norm_DJF.mean('time')\n",
    "\n",
    "\n",
    "lat = ds.lat.values\n",
    "lon = ds.lon.values\n",
    "\n",
    "#use the subregion co-ordinates specific to your region. \n",
    "#And you need to first select the subregion before doing any calculations at all.\n",
    "#then instead of basemap and shifting the grid use this:\n",
    "#you put this immediately after calculated the mean and p values and you have defined the colormap and \n",
    "#colorbar limits. Do not use any shifting code.\n",
    "\n",
    "t_stat, p_val = stats.ttest_ind(sst_elnino_DJF.groupby('time.year').mean('time'), UHI_norm_DJF.groupby('time.year').mean('time'), equal_var=False, nan_policy='omit')\n",
    "\n",
    "p_values_new = ma.masked_greater(p_val, 0.1)\n",
    "\n",
    "levels=np.arange(-2, 2+0.05, .5)\n",
    "\n",
    "\n",
    "proj = ccrs.PlateCarree()\n",
    "x,y = np.meshgrid(lon,lat)\n",
    "\n",
    "f, ax = plt.subplots(nrows=1,subplot_kw=dict(projection=proj))\n",
    "\n",
    "ax.coastlines()\n",
    "\n",
    "\n",
    "\n",
    "h = ax.contourf(x, y, ds, transform=proj,  cmap='BrBG', levels=levels, extend='both')\n",
    "\n",
    "plt.rcParams['hatch.color'] = 'magenta'\n",
    "#ax.contourf(x, y, p_values_new,transform=proj,  hatches=[\"....\"], alpha=0)\n",
    "\n",
    "\n",
    "#ax.set_yticks(np.linspace(-90, 90, 5), crs=proj)\n",
    "#ax.set_xticks(np.linspace(-130, -60, 8), crs=proj)\n",
    "#ax.set_yticks(np.linspace(20, 55, 8), crs=proj)\n",
    "\n",
    "ax.set_xticks(np.linspace(60, 95, 8), crs=proj)\n",
    "ax.set_yticks(np.linspace(5, 30, 6), crs=proj)\n",
    "lon_formatter = LongitudeFormatter()\n",
    "lat_formatter = LatitudeFormatter()\n",
    "ax.xaxis.set_major_formatter(lon_formatter)\n",
    "ax.yaxis.set_major_formatter(lat_formatter)\n",
    "\n",
    "\n",
    "\n",
    "divider = make_axes_locatable(ax)\n",
    "ax_cb = divider.new_horizontal(size=\"3%\", pad=0.3, axes_class=plt.Axes)\n",
    "\n",
    "\n",
    "f.add_axes(ax_cb)\n",
    "plt.colorbar(h, cax=ax_cb)\n",
    "\n",
    "\n",
    "#plt.rcParams['hatch.color'] = 'lime'\n",
    "#plt.rcParams['hatch.linewidth'] = 1.5\n",
    "#m.contourf(xi, yi, p_new, hatches=[\"....\"], alpha=0)\n",
    "\n",
    "#plt.rcParams['figure.facecolor'] = 'w'\n",
    "\n",
    "\n",
    "#cb = plt.colorbar(cs, orientation='horizontal', pad=0.10, shrink=0.9)\n",
    "plt.tight_layout()\n",
    "\n",
    "ax.set_title('El Nino DJF Prec Anomaly fixed', fontsize=16, y=1.0, pad=-14)\n",
    "#plt.show()\n",
    "#plt.savefig('/tigress/emcmahon/ST/regional_UHI_composites/DJF_elnino_prec',dpi=600) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'La Nina JJA Prec Anomaly w')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = sst_lanina_JJA.mean('time') - UHI_norm_JJA.mean('time')\n",
    "\n",
    "#print(ds)\n",
    "\n",
    "#ds = da.sel(lon=slice(61,97), lat=slice(5,30))\n",
    "\n",
    "#print(ds)\n",
    "lat = ds.lat.values\n",
    "lon = ds.lon.values\n",
    "\n",
    "#use the subregion co-ordinates specific to your region. \n",
    "#And you need to first select the subregion before doing any calculations at all.\n",
    "#then instead of basemap and shifting the grid use this:\n",
    "#you put this immediately after calculated the mean and p values and you have defined the colormap and \n",
    "#colorbar limits. Do not use any shifting code.\n",
    "\n",
    "t_stat, p_val = stats.ttest_ind(sst_lanina_JJA.groupby('time.year').mean('time'), UHI_norm_JJA.groupby('time.year').mean('time'), equal_var=False, nan_policy='omit')\n",
    "\n",
    "p_values_new = ma.masked_greater(p_val, 0.1)\n",
    "\n",
    "levels=np.arange(-2, 2+0.05, .5)\n",
    "\n",
    "\n",
    "proj = ccrs.PlateCarree()\n",
    "x,y = np.meshgrid(lon,lat)\n",
    "\n",
    "f, ax = plt.subplots(nrows=1,subplot_kw=dict(projection=proj))\n",
    "\n",
    "ax.coastlines()\n",
    "\n",
    "\n",
    "\n",
    "h = ax.contourf(x, y, ds, transform=proj, cmap='BrBG', levels=levels, extend='both')\n",
    "plt.rcParams['hatch.color'] = 'lime'\n",
    "\n",
    "ax.contourf(x, y, p_values_new, transform=proj, hatches=[\"////\"], alpha=0)\n",
    "\n",
    "#ax.set_xticks(np.linspace(-180, 180, 5), crs=proj)\n",
    "#ax.set_yticks(np.linspace(-90, 90, 5), crs=proj)\n",
    "#ax.set_xticks(np.linspace(-130, -60, 8), crs=proj)\n",
    "#ax.set_yticks(np.linspace(20, 55, 8), crs=proj)\n",
    "\n",
    "ax.set_xticks(np.linspace(60, 95, 8), crs=proj)\n",
    "ax.set_yticks(np.linspace(5, 30, 6), crs=proj)\n",
    "lon_formatter = LongitudeFormatter()\n",
    "lat_formatter = LatitudeFormatter()\n",
    "ax.xaxis.set_major_formatter(lon_formatter)\n",
    "ax.yaxis.set_major_formatter(lat_formatter)\n",
    "\n",
    "\n",
    "\n",
    "divider = make_axes_locatable(ax)\n",
    "ax_cb = divider.new_horizontal(size=\"3%\", pad=0.3, axes_class=plt.Axes)\n",
    "\n",
    "\n",
    "f.add_axes(ax_cb)\n",
    "plt.colorbar(h, cax=ax_cb)\n",
    "\n",
    "\n",
    "\n",
    "#plt.rcParams['hatch.color'] = 'lime'\n",
    "#plt.rcParams['hatch.linewidth'] = 1.5\n",
    "#m.contourf(xi, yi, p_new, hatches=[\"....\"], alpha=0)\n",
    "\n",
    "#plt.rcParams['figure.facecolor'] = 'w'\n",
    "\n",
    "\n",
    "#cb = plt.colorbar(cs, orientation='horizontal', pad=0.10, shrink=0.9)\n",
    "plt.tight_layout()\n",
    "#plt.title('La Nina JJA Prec Anomaly', fontsize=16)\n",
    "ax.set_title('La Nina JJA Prec Anomaly w', fontsize=16, y=1.0, pad=-14)\n",
    "#plt.show()\n",
    "#plt.savefig('/tigress/emcmahon/ST/regional_UHI_composites/JJA_lanina_prec',dpi=600) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'La Nina JJA Prec Anomaly fixed')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = sst_lanina_JJA.mean('time') - UHI_norm_JJA.mean('time')\n",
    "\n",
    "#print(ds)\n",
    "\n",
    "#ds = da.sel(lon=slice(61,97), lat=slice(5,30))\n",
    "\n",
    "#print(ds)\n",
    "lat = ds.lat.values\n",
    "lon = ds.lon.values\n",
    "\n",
    "#use the subregion co-ordinates specific to your region. \n",
    "#And you need to first select the subregion before doing any calculations at all.\n",
    "#then instead of basemap and shifting the grid use this:\n",
    "#you put this immediately after calculated the mean and p values and you have defined the colormap and \n",
    "#colorbar limits. Do not use any shifting code.\n",
    "\n",
    "t_stat, p_val = stats.ttest_ind(sst_lanina_JJA.groupby('time.year').mean('time'), UHI_norm_JJA.groupby('time.year').mean('time'), equal_var=False, nan_policy='omit')\n",
    "\n",
    "p_values_new = ma.masked_greater(p_val, 0.1)\n",
    "\n",
    "levels=np.arange(-2, 2+0.05, .5)\n",
    "\n",
    "\n",
    "proj = ccrs.PlateCarree()\n",
    "x,y = np.meshgrid(lon,lat)\n",
    "\n",
    "f, ax = plt.subplots(nrows=1,subplot_kw=dict(projection=proj))\n",
    "\n",
    "ax.coastlines()\n",
    "\n",
    "\n",
    "\n",
    "h = ax.contourf(x, y, ds, transform=proj,  cmap='BrBG', levels=levels, extend='both')\n",
    "plt.rcParams['hatch.color'] = 'magenta'\n",
    "\n",
    "ax.contourf(x, y, p_values_new,transform=proj,  hatches=[\"....\"], alpha=0)\n",
    "\n",
    "#ax.set_xticks(np.linspace(-180, 180, 5), crs=proj)\n",
    "#ax.set_yticks(np.linspace(-90, 90, 5), crs=proj)\n",
    "#ax.set_xticks(np.linspace(-130, -60, 8), crs=proj)\n",
    "#ax.set_yticks(np.linspace(20, 55, 8), crs=proj)\n",
    "\n",
    "ax.set_xticks(np.linspace(60, 95, 8), crs=proj)\n",
    "ax.set_yticks(np.linspace(5, 30, 6), crs=proj)\n",
    "lon_formatter = LongitudeFormatter()\n",
    "lat_formatter = LatitudeFormatter()\n",
    "ax.xaxis.set_major_formatter(lon_formatter)\n",
    "ax.yaxis.set_major_formatter(lat_formatter)\n",
    "\n",
    "\n",
    "\n",
    "divider = make_axes_locatable(ax)\n",
    "ax_cb = divider.new_horizontal(size=\"3%\", pad=0.3, axes_class=plt.Axes)\n",
    "\n",
    "\n",
    "f.add_axes(ax_cb)\n",
    "plt.colorbar(h, cax=ax_cb)\n",
    "\n",
    "\n",
    "\n",
    "#plt.rcParams['hatch.color'] = 'lime'\n",
    "#plt.rcParams['hatch.linewidth'] = 1.5\n",
    "#m.contourf(xi, yi, p_new, hatches=[\"....\"], alpha=0)\n",
    "\n",
    "#plt.rcParams['figure.facecolor'] = 'w'\n",
    "\n",
    "\n",
    "#cb = plt.colorbar(cs, orientation='horizontal', pad=0.10, shrink=0.9)\n",
    "plt.tight_layout()\n",
    "#plt.title('La Nina JJA Prec Anomaly', fontsize=16)\n",
    "ax.set_title('La Nina JJA Prec Anomaly fixed', fontsize=16, y=1.0, pad=-14)\n",
    "#plt.show()\n",
    "#plt.savefig('/tigress/emcmahon/ST/regional_UHI_composites/JJA_lanina_prec',dpi=600) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'La Nina DJF Prec Anomaly fixed')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = sst_lanina_DJF.mean('time') - UHI_norm_DJF.mean('time')\n",
    "\n",
    "#print(ds)\n",
    "\n",
    "#ds = da.sel(lon=slice(61,97), lat=slice(5,30))\n",
    "\n",
    "#print(ds)\n",
    "lat = ds.lat.values\n",
    "lon = ds.lon.values\n",
    "\n",
    "#use the subregion co-ordinates specific to your region. \n",
    "#And you need to first select the subregion before doing any calculations at all.\n",
    "#then instead of basemap and shifting the grid use this:\n",
    "#you put this immediately after calculated the mean and p values and you have defined the colormap and \n",
    "#colorbar limits. Do not use any shifting code.\n",
    "\n",
    "t_stat, p_val = stats.ttest_ind(sst_lanina_DJF.groupby('time.year').mean('time'), UHI_norm_DJF.groupby('time.year').mean('time'), equal_var=False, nan_policy='omit')\n",
    "\n",
    "p_values_new = ma.masked_greater(p_val, 0.1)\n",
    "\n",
    "#levels=np.arange(-.4, .4+0.05, .1)\n",
    "levels=np.arange(-2, 2+0.05, .5)\n",
    "\n",
    "proj = ccrs.PlateCarree()\n",
    "x,y = np.meshgrid(lon,lat)\n",
    "\n",
    "f, ax = plt.subplots(nrows=1,subplot_kw=dict(projection=proj))\n",
    "\n",
    "ax.coastlines()\n",
    "\n",
    "\n",
    "\n",
    "h = ax.contourf(x, y, ds, transform=proj,  cmap='BrBG', levels=levels, extend='both')\n",
    "plt.rcParams['hatch.color'] = 'magenta'\n",
    "\n",
    "ax.contourf(x, y, p_values_new,transform=proj,  hatches=[\"....\"], alpha=0)\n",
    "\n",
    "#ax.set_xticks(np.linspace(-180, 180, 5), crs=proj)\n",
    "#ax.set_yticks(np.linspace(-90, 90, 5), crs=proj)\n",
    "#ax.set_xticks(np.linspace(-130, -60, 8), crs=proj)\n",
    "#ax.set_yticks(np.linspace(20, 55, 8), crs=proj)\n",
    "\n",
    "ax.set_xticks(np.linspace(60, 95, 8), crs=proj)\n",
    "ax.set_yticks(np.linspace(5, 30, 6), crs=proj)\n",
    "lon_formatter = LongitudeFormatter()\n",
    "lat_formatter = LatitudeFormatter()\n",
    "ax.xaxis.set_major_formatter(lon_formatter)\n",
    "ax.yaxis.set_major_formatter(lat_formatter)\n",
    "\n",
    "\n",
    "\n",
    "divider = make_axes_locatable(ax)\n",
    "ax_cb = divider.new_horizontal(size=\"3%\", pad=0.3, axes_class=plt.Axes)\n",
    "\n",
    "\n",
    "f.add_axes(ax_cb)\n",
    "plt.colorbar(h, cax=ax_cb)\n",
    "\n",
    "\n",
    "\n",
    "#plt.rcParams['hatch.linewidth'] = 1.5\n",
    "#m.contourf(xi, yi, p_new, hatches=[\"....\"], alpha=0)\n",
    "\n",
    "#plt.rcParams['figure.facecolor'] = 'w'\n",
    "\n",
    "\n",
    "#cb = plt.colorbar(cs, orientation='horizontal', pad=0.10, shrink=0.9)\n",
    "plt.tight_layout()\n",
    "#plt.title('La Nina DJF Prec Anomaly', fontsize=16)\n",
    "ax.set_title('La Nina DJF Prec Anomaly fixed', fontsize=16, y=1.0, pad=-14)\n",
    "#plt.show()\n",
    "#plt.savefig('/tigress/emcmahon/ST/regional_UHI_composites/DJF_lanina_prec',dpi=600) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}